Qwen Arabic Fine-tuning Project

This project fine-tunes the Qwen2-1.5B model for Arabic language tasks using Quantized LoRA (QLoRA).

Qwen-Arabic Evaluation on ArabicMMLU

Eevaluation of the Qwen-Arabic language model (1.5B parameters) on the ArabicMMLU benchmark. The model demonstrates strong parameter efficiency while maintaining competitive performance across various knowledge domains.

Model Overview

Qwen-Arabic is a 1.5B parameter language model fine-tuned for Arabic language tasks. It is based on the Qwen architecture and optimized using QLoRA (Quantized Low-Rank Adaptation) techniques.

Performance Results

Overall Performance

• Average Accuracy: 42.3%
• Best Category: Social Science (46.1%)
• Most Challenging: Arabic Language (37.8%)

Category-wise Performance

Efficiency Analysis

• Performance per Billion Parameters: 28.20 accuracy points
• 389.0x more parameter-efficient than GPT-4
• Achieves 58.3% of GPT-4’s performance with only 0.15% of parameters

Comparison with Other Models

Prerequisites

• Ubuntu (or similar Linux distribution)
• Python 3.10
• CUDA-compatible GPU with at least 4GB VRAM
• At least 12GB system RAM
• Ollama installed and configured

Setup

1. Clone this repository:

   git clone https://github.com/prakash-aryan/qwen-arabic-project.git
   cd qwen-arabic-project
   

2. Create and activate a virtual environment:

   python3.10 -m venv qwen_env
   source qwen_env/bin/activate
   

3. Install the required packages:

   pip install --upgrade pip
   pip install -r requirements.txt
   

4. Install PyTorch with CUDA support:

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
   

Project Structure

qwen-arabic-project/
├── data/
│   └── arabic_instruction_dataset/
├── models/
├── results/
├── src/
│   ├── compare_qwen_models.py
│   ├── evaluate_arabic_model.py
│   ├── finetune_qwen.py
│   ├── get_datasets.py
│   ├── load_and_merge_model.py
│   ├── preprocess_datasets.py
│   └── validate_dataset.py
├── tools/
│   └── llama-quantize
├── requirements.txt
├── run_pipeline.sh
├── Modelfile
└── README.md

Usage

1. Download and prepare datasets:

   python src/get_datasets.py
   

2. Preprocess and combine datasets:

   python src/preprocess_datasets.py
   

3. Validate the dataset:

   python src/validate_dataset.py
   

4. Fine-tune the model:

   python src/finetune_qwen.py --data_path ./data/arabic_instruction_dataset --output_dir ./models/qwen2_arabic_finetuned --num_epochs 3 --batch_size 1 --gradient_accumulation_steps 16 --learning_rate 2e-5
   

5. Load and merge the fine-tuned model:

   python src/load_and_merge_model.py
   

6. Convert to GGUF format:

   python src/convert_hf_to_gguf.py ./models/qwen2_arabic_merged_full --outfile ./models/qwen_arabic_merged_full.gguf
   

7. Quantize the model:

   ./tools/llama-quantize ./models/qwen_arabic_merged_full.gguf ./models/qwen_arabic_merged_full_q4_k_m.gguf q4_k_m
   

8. Create Ollama model:

   ollama create qwen-arabic-custom -f Modelfile
   

9. Evaluate the model:

   python src/evaluate_arabic_model.py
   

10. Compare models:

    python src/compare_qwen_models.py
    

Running the Full Pipeline

To run the entire pipeline from data preparation to model evaluation, use the provided shell script:

chmod +x run_pipeline.sh
./run_pipeline.sh

Notes

• Ensure you have sufficient disk space for the datasets and model files.
• The fine-tuning process can take several hours to days, depending on your hardware.
• Monitor GPU memory usage during fine-tuning and adjust batch size or gradient accumulation steps if necessary.
• Make sure to have Ollama installed for the model creation and evaluation steps.

Troubleshooting

• If you encounter CUDA out-of-memory errors, try reducing the batch size or increasing gradient accumulation steps.
• For any other issues, please check the error logs or open an issue in the GitHub repository.

License

This project is licensed under the GNU General Public License v3.0 (GPL-3.0).

This means: - You can use, modify, and distribute this software. - If you distribute modified versions, you must also distribute them under the GPL-3.0. - You must include the original copyright notice and the license text. - You must disclose your source code when you distribute the software. - There’s no warranty for this free software.

For more details, see the LICENSE file in this repository or visit GNU GPL v3.0.

Acknowledgements

This project uses the following main libraries and tools: - Transformers by Hugging Face - PyTorch - PEFT (Parameter-Efficient Fine-Tuning) - Ollama - GGUF (for model conversion)